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Weight quantization is used to deploy high-performance deep learning models on resource-limited hardware, enabling the use of low-precision integers for storage and computation.
Pruning versus clipping in neural networks
Steven A Janowsky · 1989
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The effects of quantization on multilayer neural networks
Gunhan Dundar and Kenneth Rose · 1995
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An analysis of noise in recurrent neural networks: convergence and generalization
Kam-Chuen Jim, C.L. Giles, and B.G. Horne · 1996
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Quantization noise improvement in a hybrid distributed-neuron ann architecture
H. Djahanshahi, M. Ahmadi, G.A. Jullien, and W.C. Miller · 2001
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Estimating or propagating gradients through stochastic neurons for conditional computation, 2013
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Efficient implementation of stdp rules on spinnaker neuromorphic hardware
Peter U. Diehl and Matthew Cook · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Big/little deep neural network for ultra low power inference
Eunhyeok Park, Dongyoung Kim, Soobeom Kim, Yong-Deok Kim, Gunhee Kim, Sungroh Yoon, and Sungjoo Yoo · 2015
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Song Han, Huizi Mao, and William J Dally · 2015
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Backpropagation for energy-efficient neuromorphic computing
Steve K Esser, Rathinakumar Appuswamy, Paul Merolla, John V Arthur, and Dharmendra S Modha · 2015
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Spiking deep networks with lif neurons, 2015
Eric Hunsberger and Chris Eliasmith · 2015
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Branchynet: Fast inference via early exiting from deep neural networks
Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations, 2016
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Ternary neural networks for resource-efficient ai applications
Hande Alemdar, Vincent Leroy, Adrien Prost-Boucle, and Frédéric Pétrot · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Balanced quantization: An effective and efficient approach to quantized neural networks
Shu-Chang Zhou, Yu-Zhi Wang, He Wen, Qin-Yao He, and Yu-Heng Zou · 2017
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Event-driven random back-propagation: Enabling neuromorphic deep learning machines
Emre O Neftci, Charles Augustine, Somnath Paul, and Georgios Detorakis · 2017
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Regularizing deep neural networks by noise: Its interpretation and optimization
Hyeonwoo Noh, Tackgeun You, Jonghwan Mun, and Bohyung Han · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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A low power, fully event-based gesture recognition system
Arnon Amir, Brian Taba, David Berg, Timothy Melano, Jeffrey McKinstry, Carmelo Di Nolfo, Tapan Nayak, Alexander Andreopoulos, Guillaume Garreau, Marcela Mendoza, Jeff Kusnitz, Michael Debole, Steve Esser, Tobi Delbruck, Myron Flickner, and Dharmendra Modha · 2017
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Sgdr: Stochastic gradient descent with warm restarts, 2017
Ilya Loshchilov and Frank Hutter · 2017
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Model compression and acceleration for deep neural networks: The principles, progress, and challenges
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang · 2018
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Deep learning with spiking neurons: Opportunities and challenges
Michael Pfeiffer and Thomas Pfeil · 2018
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Neuromorphic vision hybrid rram-cmos architecture
Jason Kamran Eshraghian, Kyoungrok Cho, Ciyan Zheng, Minho Nam, Herbert Ho-Ching Iu, Wen Lei, and Kamran Eshraghian · 2018
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Slayer: Spike layer error reassignment in time
Sumit B Shrestha and Garrick Orchard · 2018
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Loihi: A neuromorphic manycore processor with on-chip learning
Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, et al · 2018
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Loihi: A neuromorphic manycore processor with on-chip learning
Spatio-temporal pruning and quantization for low-latency spiking neural networks, 2021
Sayeed Shafayet Chowdhury, Isha Garg, and Kaushik Roy · 2021
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Reintroducing straight-through estimators as principled methods for stochastic binary networks, 2021
Alexander Shekhovtsov and Viktor Yanush · 2021
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Q-SpiNN: A framework for quantizing spiking neural networks
Rachmad Vidya Wicaksana Putra and Muhammad Shafique · 2021
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Loss aware post-training quantization
Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M Bronstein, and Avi Mendelson · 2021
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Dendrocentric learning for synthetic intelligence
Kwabena Boahen · 2022
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Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, Yuyun Liao, Chit-Kwan Lin, Andrew Lines, Ruokun Liu, Deepak Mathaikutty, Steven McCoy, Arnab Paul, Jonathan Tse, Guruguhanathan Venkataramanan, Yi-Hsin Weng, Andreas Wild, Yoonseok Yang, and Hong Wang · 2018
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Bag of tricks for image classification with convolutional neural networks, 2018
Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li · 2018
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Resource efficient 3d convolutional neural networks
Okan Kopuklu, Neslihan Kose, Ahmet Gunduz, and Gerhard Rigoll · 2019
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Towards spike-based machine intelligence with neuromorphic computing
Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda · 2019
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Selection and optimization of temporal spike encoding methods for spiking neural networks
Balint Petro, Nikola Kasabov, and Rita M Kiss · 2019
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Approximating back-propagation for a biologically plausible local learning rule in spiking neural networks
Amar Shrestha, Haowen Fang, Qing Wu, and Qinru Qiu · 2019
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Adversarial noise layer: Regularize neural network by adding noise
Zhonghui You, Jinmian Ye, Kunming Li, Zenglin Xu, and Ping Wang · 2019
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Alexander Henkes, Jason K Eshraghian, and Henning Wessels · 2022
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Navigating local minima in quantized spiking neural networks, 2022
Jason K. Eshraghian, Corey Lammie, Mostafa Rahimi Azghadi, and Wei D. Lu · 2022
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Memristor-based binarized spiking neural networks: Challenges and applications
Jason K Eshraghian, Xinxin Wang, and Wei D Lu · 2022
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The heidelberg spiking data sets for the systematic evaluation of spiking neural networks
Benjamin Cramer, Yannik Stradmann, Johannes Schemmel, and Friedemann Zenke · 2022
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Nonuniform-to-uniform quantization: Towards accurate quantization via generalized straight-through estimation, 2022
Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric Xing, and Zhiqiang Shen · 2022
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Training discrete deep generative models via gapped straight-through estimator, 2022
Ting-Han Fan, Ta-Chung Chi, Alexander I. Rudnicky, and Peter J. Ramadge · 2022
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Brain-inspired learning in artificial neural networks: a review
Samuel Schmidgall, Jascha Achterberg, Thomas Miconi, Louis Kirsch, Rojin Ziaei, S Hajiseyedrazi, and Jason Eshraghian · 2023
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Neuromorphic deep spiking neural networks for seizure detection
Yikai Yang, Jason K Eshraghian, Nhan Duy Truong, Armin Nikpour, and Omid Kavehei · 2023
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Spiking neural networks for frame-based and event-based single object localization
Sami Barchid, José Mennesson, Jason Eshraghian, Chaabane Djéraba, and Mohammed Bennamoun · 2023
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Spikegpt: Generative pre-trained language model with spiking neural networks
Rui-Jie Zhu, Qihang Zhao, Guoqi Li, and Jason K Eshraghian · 2023
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Sub-mw neuromorphic snn audio processing applications with rockpool and xylo
Hannah Bos and Dylan Muir · 2023
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Ole Richter, Yannan Xing, Michele De Marchi, Carsten Nielsen, Merkourios Katsimpris, Roberto Cattaneo, Yudi Ren, Qian Liu, Sadique Sheik, Tugba Demirci, et al · 2023
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Jens E Pedersen, Steven Abreu, Matthias Jobst, Gregor Lenz, Vittorio Fra, Felix C Bauer, Dylan R Muir, Peng Zhou, Bernhard Vogginger, Kade Heckel, et al · 2023
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Xilinx/brevitas, 2023
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Optically tunable electrical oscillations in oxide-based memristors for neuromorphic computing
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